Areas for Development in ICNAP: From From Data Acquisition to a Digital Business Model

To ensure that value chains for manufacturing of complex and customized products can become considerably more flexible and efficient than before, ICNAP places research questions affecting seven subject areas at the focus of its development efforts.

Sensor Systems and Data Acquisition

How are all relevant data along the entire process chain collected?

Data acquisition is the first step on the way to networked, adaptive production. It is vital to establish which data can be recorded directly in the control unit of the production machine concerned and which can only be obtained via additional sensors. When additional sensors are required to measure parameters such as temperature, speed or pressure for example, these must be integrated robustly and reliably within the manufacturing process.

Interfaces and Connectivity

How do the key players involved and systems communicate with one another during the production process?

When the production data are reliably recorded, a suitable communications protocol such as MQTT or OPC-UA is selected and the decision is made as to whether communication should be via wired or wireless connection. The ICNAP partners scrutinize the data transfer requirements in terms of data throughput and latency for the production process in hand and can already issue recommendations at this point.

Data Synchronization and Middleware

How are raw data time-synchronized and enriched with information?

Once communication has been established within the production process, it is essential to determine how to standardize procedures for filing data from various sources simultaneously and at various recording frequencies. The quality of the different sources and the required level of data accuracy are also defined at this point in order to facilitate accurate comparison and evaluation of analysis results within the networked production environment.

Data Modelling and Data Analytics

How are the relevant data selected and what methods are used to obtain information from that data?

To provide even better support to staff working in manufacturing companies – from production planners through quality managers to machine operators - whenever they have decisions to make, it is vital to define which process chain data are relevant specifically to them. This is achieved by modelling the actual process digitally on the basis of the data obtained. Information can be generated from the data underpinning the structure of process knowledge using suitable data analytics methods in the form of machine-learning algorithms or correlation analyses.

Digital Twin in the Product Life Cycle

How is information and know-how at different stages in the product life cycle brought together digitally and visualized?

Before the information recorded and the know-how acquired in the course of the entire value chain can be stored and used, the relevant data have to be brought together and set in relation to one another. The outcome is a digital image of the actual process which can be used to visualize these data in a goal-oriented and user-friendly form. Recommendations for action and feedback strategies for production can be derived from the knowledge obtained about the process.  

Cloud Systems and IT Architecture

How is the most suitable infrastructure selected and the existing systems connected to it?  

An efficient IT architecture is vital to the successful integration of digital tools in production. To this end, ICNAP tests matching database and cloud systems for in-company or cross-company networks, taking account of current safety standards, norms and regulations.

Digital Business Models

How are digital business models developed and integrated within the traditional product and services portfolio?

Not only does networked, adaptive production improve production by making additional knowledge available but it also opens up the possibility of completely new forms of economic value added via extended or completely new physical processes and products. Digital and data-based business models can be developed, classified and evaluated even for digital services such as machining-as-a-service or power-by-the-hour.